# awesome-speech-recognition-speech-synthesis-papers

**Repository Path**: tiandijianji/awesome-speech-recognition-speech-synthesis-papers

## Basic Information

- **Project Name**: awesome-speech-recognition-speech-synthesis-papers
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-12-22
- **Last Updated**: 2021-12-23

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# awesome-speech-recognition-speech-synthesis-papers
I'm a research engineer doing speech synthesis at Tencent Wechat iHearing Group. I worked on automatic speech recognition, expressive speech synthesis, few-shot speech synthesis, voice conversion, singing voice synthesis and few-shot singing voice synthesis. I collect some papers which I think very interesting in speech field, I hope this project will help you. If you have any questions, welcome to send email to me: zzw922cn@gmail.com Thank you for your stars!

## Paper List
- [Automatic Speech Recognition(ASR)](#Automatic-Speech-Recognition)
- [Speaker Verification](#Speaker-Verification)
- [Voice Conversion(VC)](#Voice-Conversion)
- [Speech Synthesis(TTS)](#Speech-Synthesis)
- [Language Modelling](#Language-Modelling)
- [Confidence Estimates](#Confidence-Estimates)
- [Music Modelling](#Music-Modelling)
- [Interesting papers](#Interesting-papers)


### Automatic Speech Recognition
- **An Introduction to the Application of the Theory of Probabilistic Functions of a Markov Process to Automatic Speech Recognition**(1982), S. E. LEVINSON et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6768244)

- **A Maximum Likelihood Approach to Continuous Speech Recognition**(1983), LALIT R. BAHL et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4767370&tag=1)

- **Heterogeneous Acoustic Measurements and Multiple Classifiers for Speech Recognition**(1986), Andrew K. Halberstadt. [[pdf]](https://groups.csail.mit.edu/sls/publications/1998/phdthesis-drew.pdf)

- **Maximum Mutual Information Estimation of Hidden Markov Model Parameters for Speech Recognition**(1986), Lalit R. Bahi et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1169179)

- **A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition**(1989), Lawrence R Rabiner. [[pdf]](https://pdfs.semanticscholar.org/fb04/6159dfb4a2beb95756fe1116056a6d922565.pdf?\_ga=2.37020706.362861000.1494045851-921183529.1494045851)

- **Phoneme recognition using time-delay neural networks**(1989), Alexander H. Waibel et al. [[pdf]](https://pdfs.semanticscholar.org/b554/da42487697cb0d01a4146858e966c1d2404f.pdf?\_ga=2.97032540.235965811.1494658719-1308334183.1494658711)

- **Speaker-independent phone recognition using hidden Markov models**(1989), Kai-Fu Lee et al. [[pdf]](http://repository.cmu.edu/cgi/viewcontent.cgi?article=2768&context=compsci)

- **Hidden Markov Models for Speech Recognition**(1991), B. H. Juang et al. [[pdf]](http://www.jstor.org/stable/1268779)

- **Review of Tdnn (time Delay Neural Network) Architectures for Speech Recognition**(2014), Masahide Sugiyamat et al. [[pdf]](https://pdfs.semanticscholar.org/073b/6128f04fe4b88b88ae297615af289c308753.pdf?\_ga=2.103860032.1725061846.1494658711-1308334183.1494658711)

- **Connectionist Speech Recognition: A Hybrid Approach**(1994), Herve Bourlard et al. [[pdf]](https://www.researchgate.net/profile/Herve\_Bourlard/publication/230875873\_Connectionist\_Speech\_Recognition\_A\_Hybrid\_Approach/links/0deec5149eb889b8c7000000/Connectionist-Speech-Recognition-A-Hybrid-Approach.pdf)

- **A post-processing system to yield reduced word error rates: Recognizer Output Voting Error Reduction (ROVER)**(1997), J.G. Fiscus. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=659110)

- **Speech recognition with weighted finite-state transducers**(2001), M Mohri et al. [[pdf]](https://cs.nyu.edu/~mohri/pub/hbka.pdf)

- **Framewise phoneme classification with bidirectional LSTM and other neural network architectures**(2005), Alex Graves et al. [[pdf]](https://pdfs.semanticscholar.org/83d6/1d9b71a838aa150d7ef232dc6d4c73e24250.pdf?\_ga=1.187838062.730356906.1493526584)

- **Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks**(2006), Alex Graves et al. [[pdf]](https://pdfs.semanticscholar.org/daed/0db4538e1a83b4680545b44e3083843168e7.pdf?\_ga=1.45211874.730356906.1493526584)

- **The kaldi speech recognition toolkit**(2011), Daniel Povey et al. [[pdf]](http://publications.idiap.ch/downloads/reports/2011/Povey\_Idiap-RR-04-2012.pdf)

- **Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition**(2012), Ossama Abdel-Hamid et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6288864)

- **Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition**(2012), George E. Dahl et al. [[pdf]](http://ieeexplore.ieee.org/document/5740583/?part=1)

- **Deep Neural Networks for Acoustic Modeling in Speech Recognition**(2012), Geoffrey Hinton et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6296526)

- **Sequence Transduction with Recurrent Neural Networks**(2012), Alex Graves et al. [[pdf]](https://arxiv.org/pdf/1211.3711.pdf)

- **Deep convolutional neural networks for LVCSR**(2013), Tara N. Sainath et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6639347)

- **Improving deep neural networks for LVCSR using rectified linear units and dropout**(2013), George E. Dahl et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6639346)

- **Improving low-resource CD-DNN-HMM using dropout and multilingual DNN training**(2013), Yajie Miao et al. [[pdf]](https://pdfs.semanticscholar.org/a818/a229c70161d6e46f9861bb5b7d59065d3982.pdf?\_ga=1.187845614.730356906.1493526584)

- **Improvements to deep convolutional neural networks for LVCSR**(2013), Tara N. Sainath et al. [[pdf]](https://pdfs.semanticscholar.org/b299/c8878276d837f9417eb4760ad7b69edb0b58.pdf?\_ga=1.150662000.730356906.1493526584)

- **Machine Learning Paradigms for Speech Recognition: An Overview**(2013), Li Deng et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6423821)

- **Recent advances in deep learning for speech research at Microsoft**(2013), Li Deng et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6639345)

- **Speech recognition with deep recurrent neural networks**(2013), Alex Graves et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6638947)

- **Convolutional deep maxout networks for phone recognition**(2014), László Tóth et al. [[pdf]](https://pdfs.semanticscholar.org/0a24/5098455a6663f922a83d318f7b61d357ab1f.pdf?\_ga=1.218359519.730356906.1493526584)

- **Convolutional Neural Networks for Speech Recognition**(2014), Ossama Abdel-Hamid et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6857341)

- **Combining time- and frequency-domain convolution in convolutional neural network-based phone recognition**(2014), László Tóth. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6853584)

- **Deep Speech: Scaling up end-to-end speech recognition**(2014), Awni Y. Hannun et al. [[pdf]](https://arxiv.org/pdf/1412.5567.pdf)

- **End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results**(2014), Jan Chorowski et al. [[pdf]](https://arxiv.org/pdf/1412.1602.pdf)

- **First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs**(2014), Andrew L. Maas et al. [[pdf]](https://arxiv.org/pdf/1408.2873.pdf)

- **Long short-term memory recurrent neural network architectures for large scale acoustic modeling**(2014), Hasim Sak et al. [[pdf]](https://pdfs.semanticscholar.org/c85d/46a94768bdcf7ffcb844b47c5b8e8e8234a3.pdf?\_ga=1.8585459.730356906.1493526584)

- **Robust CNN-based speech recognition with Gabor filter kernels**(2014), Shuo-Yiin Chang et al. [[pdf]](https://pdfs.semanticscholar.org/1d34/0fe19026b0359bde23fcd7299a99a240bd15.pdf?\_ga=1.184683503.730356906.1493526584)

- **Stochastic pooling maxout networks for low-resource speech recognition**(2014), Meng Cai et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6854204)

- **Towards End-to-End Speech Recognition with Recurrent Neural Networks**(2014), Alex Graves et al. [[pdf]](https://pdfs.semanticscholar.org/0fa5/53cfa0cf3cbdf7a913aa2ae789a757dfb32f.pdf?\_ga=1.214035281.730356906.1493526584)

- **A neural transducer**(2015), N Jaitly et al. [[pdf]](https://arxiv.org/abs/1511.04868)

- **Attention-Based Models for Speech Recognition**(2015), Jan Chorowski et al. [[pdf]](https://pdfs.semanticscholar.org/b624/504240fa52ab76167acfe3156150ca01cf3b.pdf?\_ga=1.50080608.730356906.1493526584)

- **Analysis of CNN-based speech recognition system using raw speech as input**(2015), Dimitri Palaz et al. [[pdf]](https://pdfs.semanticscholar.org/31f5/36e48482fc273d521525604606f417638881.pdf?\_ga=1.213722706.730356906.1493526584)

- **Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks**(2015), Tara N. Sainath et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7178838)

- **Deep convolutional neural networks for acoustic modeling in low resource languages**(2015), William Chan et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7178332)

- **Deep Neural Networks for Single-Channel Multi-Talker Speech Recognition**(2015), Chao Weng et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7122291)

- **EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding**(2015), Y Miao et al. [[pdf]](https://arxiv.org/pdf/1507.08240.pdf)

- **Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition**(2015), Hasim Sak et al. [[pdf]](https://pdfs.semanticscholar.org/9fca/2af9a0e3f2c5c3ed47abb3ebd21b7265ac2b.pdf?\_ga=1.222094174.730356906.1493526584)

- **Lexicon-Free Conversational Speech Recognition with Neural Networks**(2015), Andrew L. Maas et al. [[pdf]](https://pdfs.semanticscholar.org/55ee/875b9039febd378a3f8ac4e3d7603f83d57c.pdf?\_ga=2.128588684.1093285980.1494121465-1276580355.1494121465)

- **Online Sequence Training of Recurrent Neural Networks with Connectionist Temporal Classification**(2015), Kyuyeon Hwang et al. [[pdf]](https://arxiv.org/pdf/1511.06841.pdf)

- **Advances in All-Neural Speech Recognition**(2016), Geoffrey Zweig et al. [[pdf]](https://arxiv.org/pdf/1609.05935.pdf)

- **Advances in Very Deep Convolutional Neural Networks for LVCSR**(2016), Tom Sercu et al. [[pdf]](https://pdfs.semanticscholar.org/76b1/791f2d2776c4d3dd671b7e4f2a9fb3575703.pdf?\_ga=1.150210288.730356906.1493526584)

- **End-to-end attention-based large vocabulary speech recognition**(2016), Dzmitry Bahdanau et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7472618)

- **Deep Convolutional Neural Networks with Layer-Wise Context Expansion and Attention**(2016), Dong Yu et al. [[pdf]](https://pdfs.semanticscholar.org/8926/fa45d9fc76523766a9d65e2c3b4a9c3feb88.pdf?\_ga=1.37888869.730356906.1493526584)

- **Deep Speech 2: End-to-End Speech Recognition in English and Mandarin**(2016), Dario Amodei et al. [[pdf]](https://pdfs.semanticscholar.org/c2ba/9d550bbfb542e9fdd6e817e9be15585d0f47.pdf?\_ga=1.248137409.730356906.1493526584)

- **End-to-end attention-based distant speech recognition with Highway LSTM**(2016), Hassan Taherian. [[pdf]](https://arxiv.org/pdf/1610.05361.pdf)

- **Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning**(2016), Suyoun Kim et al. [[pdf]](https://arxiv.org/pdf/1609.06773.pdf)

- **Listen, attend and spell: A neural network for large vocabulary conversational speech recognition**(2016), William Chan et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7472621)

- **Latent Sequence Decompositions**(2016), William Chan et al. [[pdf]](https://arxiv.org/pdf/1610.03035.pdf)

- **Modeling Time-Frequency Patterns with LSTM vs. Convolutional Architectures for LVCSR Tasks**(2016), Tara N. Sainath et al. [[pdf]](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45401.pdf)

- **Recurrent Models for Auditory Attention in Multi-Microphone Distance Speech Recognition**(2016), Suyoun Kim et al. [[pdf]](https://pdfs.semanticscholar.org/b9fc/cd8bee6e6998b87b4efc671dbcee45917282.pdf?\_ga=2.168507874.235965811.1494658719-1308334183.1494658711)

- **Segmental Recurrent Neural Networks for End-to-End Speech Recognition**(2016), Liang Lu et al. [[pdf]](https://pdfs.semanticscholar.org/8477/ec32bc1dde071bed8174348da5cd6740dab0.pdf?\_ga=1.220546782.730356906.1493526584)

- **Towards better decoding and language model integration in sequence to sequence models**(2016), Jan Chorowski et al. [[pdf]](https://arxiv.org/pdf/1612.02695.pdf)

- **Very Deep Convolutional Neural Networks for Noise Robust Speech Recognition**(2016), Yanmin Qian et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7552554)

- **Very Deep Convolutional Networks for End-to-End Speech Recognition**(2016), Yu Zhang et al. [[pdf]](https://arxiv.org/pdf/1610.03022.pdf)

- **Very deep multilingual convolutional neural networks for LVCSR**(2016), Tom Sercu et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7472620)

- **Wav2Letter: an End-to-End ConvNet-based Speech Recognition System**(2016), Ronan Collobert et al. [[pdf]](https://arxiv.org/pdf/1609.03193.pdf)

- **Attentive Convolutional Neural Network based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech**(2017), Michael Neumann et al. [[pdf]](https://arxiv.org/pdf/1706.00612)

- **An enhanced automatic speech recognition system for Arabic**(2017), Mohamed Amine Menacer et al. [[pdf]](https://pdfs.semanticscholar.org/788e/b75befd9c2597f64e072cb2e86f9e7a877e4.pdf?\_ga=1.188540654.730356906.1493526584)

- **Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM**(2017), Takaaki Hori et al. [[pdf]](https://arxiv.org/pdf/1706.02737)

- **A network of deep neural networks for distant speech recognition**(2017), Mirco Ravanelli et al. [[pdf]](https://arxiv.org/pdf/1703.08002.pdf)

- **An online sequence-to-sequence model for noisy speech recognition**(2017), Chung-Cheng Chiu et al. [[pdf]](https://arxiv.org/pdf/1706.06428.pdf)

- **An Unsupervised Speaker Clustering Technique based on SOM and I-vectors for Speech Recognition Systems**(2017), Hany Ahmed et al. [[pdf]](https://pdfs.semanticscholar.org/f5be/2cb9d37e5e54c5d20644ff7025cdee14995f.pdf?\_ga=1.185419759.730356906.1493526584)

- **Attention-Based End-to-End Speech Recognition in Mandarin**(2017), C Shan et al. [[pdf]](https://arxiv.org/abs/1707.07167)

- **Building DNN acoustic models for large vocabulary speech recognition**(2017), Andrew L. Maas et al. [[pdf]](https://pdfs.semanticscholar.org/ff7b/9fbbbdc78d874fa93134d643a5a0295f648f.pdf?\_ga=1.242426692.730356906.1493526584)

- **Direct Acoustics-to-Word Models for English Conversational Speech Recognition**(2017), Kartik Audhkhasi et al. [[pdf]](https://arxiv.org/pdf/1703.07754.pdf)

- **Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments**(2017), Zixing Zhang et al. [[pdf]](https://arxiv.org/pdf/1705.10874)

- **English Conversational Telephone Speech Recognition by Humans and Machines**(2017), George Saon et al. [[pdf]](https://arxiv.org/pdf/1703.02136.pdf)

- **ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA**(2017), Song Han et al. [[pdf]](http://dl.acm.org/citation.cfm?id=3021745)

- **Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition**(2017), Chris Donahue et al. [[pdf]](https://arxiv.org/pdf/1711.05747)

- **Deep LSTM for Large Vocabulary Continuous Speech Recognition**(2017), Xu Tian et al. [[pdf]](https://arxiv.org/pdf/1703.07090.pdf)

- **Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition**(2017), Taesup Kim et al. [[pdf]](https://arxiv.org/pdf/1707.06065v1.pdf)

- **Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling**(2017), Hairong Liu et al. [[pdf]](https://arxiv.org/pdf/1703.00096.pdf)

- **Improving the Performance of Online Neural Transducer Models**(2017), Tara N. Sainath et al. [[pdf]](https://arxiv.org/pdf/1712.01807)

- **Learning Filterbanks from Raw Speech for Phone Recognition**(2017), Neil Zeghidour et al. [[pdf]](https://arxiv.org/pdf/1711.01161)

- **Multichannel End-to-end Speech Recognition**(2017), Tsubasa Ochiai et al. [[pdf]](https://arxiv.org/pdf/1703.04783.pdf)

- **Multi-task Learning with CTC and Segmental CRF for Speech Recognition**(2017), Liang Lu et al. [[pdf]](https://arxiv.org/pdf/1702.06378.pdf)

- **Multichannel Signal Processing With Deep Neural Networks for Automatic Speech Recognition**(2017), Tara N. Sainath et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7859320)

- **Multilingual Speech Recognition With A Single End-To-End Model**(2017), Shubham Toshniwal et al. [[pdf]](https://arxiv.org/pdf/1711.01694)

- **Optimizing expected word error rate via sampling for speech recognition**(2017), Matt Shannon. [[pdf]](https://arxiv.org/pdf/1706.02776)

- **Residual Convolutional CTC Networks for Automatic Speech Recognition**(2017), Yisen Wang et al. [[pdf]](https://arxiv.org/pdf/1702.07793.pdf)

- **Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition**(2017), Jaeyoung Kim et al. [[pdf]](https://arxiv.org/pdf/1701.03360.pdf)

- **Recurrent Models for Auditory Attention in Multi-Microphone Distance Speech Recognition**(2017), Suyoun Kim et al. [[pdf]](https://pdfs.semanticscholar.org/b9fc/cd8bee6e6998b87b4efc671dbcee45917282.pdf?\_ga=2.162545140.93942331.1493904208-1691509212.1493904208)

- **Reducing Bias in Production Speech Models**(2017), Eric Battenberg et al. [[pdf]](https://arxiv.org/pdf/1705.04400.pdf)

- **Robust Speech Recognition Using Generative Adversarial Networks**(2017), Anuroop Sriram et al. [[pdf]](https://arxiv.org/pdf/1711.01567)

- **State-of-the-art Speech Recognition With Sequence-to-Sequence Models**(2017), Chung-Cheng Chiu et al. [[pdf]](https://arxiv.org/pdf/1712.01769)

- **Towards Language-Universal End-to-End Speech Recognition**(2017), Suyoun Kim et al. [[pdf]](https://arxiv.org/pdf/1711.02207)

- **Accelerating recurrent neural network language model based online speech recognition system**(2018), K Lee et al. [[pdf]](https://arxiv.org/pdf/1801.09866)

- **An improved hybrid CTC-Attention model for speech recognition**(2018), Zhe Yuan et al. [[pdf]](https://arxiv.org/abs/1810.12020)

- **Hybrid CTC-Attention based End-to-End Speech Recognition using Subword Units**(2018), Zhangyu Xiao et al. [[pdf]](https://arxiv.org/abs/1807.04978)

- **SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition**, Daniel S. Park et al. [[pdf]](https://arxiv.org/pdf/1904.08779.pdf)

- **Improved Noisy Student Training for Automatic Speech Recognition**(2020), Daniel S. Park, et al. [[pdf]](https://arxiv.org/abs/2005.09629)

- **ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context** (2020),  Wei Han, et al. [[pdf]](https://arxiv.org/abs/2005.03191)

- **Conformer: Convolution-augmented Transformer for Speech Recognition**(2020), Anmol Gulati, et al. [[pdf]](https://arxiv.org/pdf/2005.08100v1.pdf)

- **On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition**(2020), Jinyu Li et al. [[pdf]](https://arxiv.org/pdf/2005.14327)

- **Efficient Training of Audio Transformers with Patchout**(2021), Khaled Koutini et al. [[pdf]](https://arxiv.org/pdf/2110.05069)

- **MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition**(2021), Linghui Meng et al. [[pdf]](https://arxiv.org/pdf/2102.12664)

- **Multi-Encoder Learning and Stream Fusion for Transformer-Based End-to-End Automatic Speech Recognition**(2021), Timo Lohrenz et al. [[pdf]](https://arxiv.org/pdf/2104.00120.pdf)

- **The History of Speech Recognition to the Year 2030**(2021), Awni Hannun et al. [[pdf]](https://arxiv.org/pdf/2108.00084.pdf)

- **Voice Conversion Can Improve ASR in Very Low-Resource Settings**(2021), Matthew Baas et al. [[pdf]](https://arxiv.org/pdf/2111.02674) 

- **Why does CTC result in peaky behavior?**(2021), Albert Zeyer et al. [[pdf]](https://arxiv.org/pdf/2105.14849.pdf)


### Speaker Verification
- **Speaker Verification Using Adapted Gaussian Mixture Models**(2000), Douglas A.Reynolds et al. [[pdf]](http://www.sciencedirect.com/science/article/pii/S1051200499903615#)

- **A tutorial on text-independent speaker verification**(2004), Frédéric Bimbot et al. [[pdf]](https://dl.acm.org/ft\_gateway.cfm?id=1289376&ftid=464492&dwn=1&CFID=843437542&CFTOKEN=31448020)

- **Deep neural networks for small footprint text-dependent speaker verification**(2014), E Variani et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6854363)

- **Deep Speaker Vectors for Semi Text-independent Speaker Verification**(2015), Lantian Li et al. [[pdf]](https://arxiv.org/pdf/1505.06427)

- **Deep Speaker: an End-to-End Neural Speaker Embedding System**(2017), Chao Li et al. [[pdf]](https://arxiv.org/pdf/1705.02304.pdf)

- **Deep Speaker Feature Learning for Text-independent Speaker Verification**(2017), Lantian Li et al. [[pdf]](https://arxiv.org/pdf/1705.03670)

- **Deep Speaker Verification: Do We Need End to End?**(2017), Dong Wang et al. [[pdf]](https://arxiv.org/pdf/1706.07859)

- **Speaker Diarization with LSTM**(2017), Quan Wang et al. [[pdf]](https://arxiv.org/pdf/1710.10468)

- **Text-Independent Speaker Verification Using 3D Convolutional Neural Networks**(2017), Amirsina Torfi et al. [[pdf]](https://arxiv.org/pdf/1705.09422)

- **End-to-End Text-Independent Speaker Verification with Triplet Loss on Short Utterances**(2017), Chunlei Zhang et al. [[pdf]](https://www.isca-speech.org/archive/Interspeech_2017/pdfs/1608.PDF)

- **Deep Neural Network Embeddings for Text-Independent Speaker Verification**(2017), David Snyder et al. [[pdf]](https://pdfs.semanticscholar.org/3697/28d7576683a25de8890e4bc02fae6132fccb.pdf)

- **Deep Discriminative Embeddings for Duration Robust Speaker Verification**(2018), Na Li et al. [[pdf]](http://www.academia.edu/download/60361072/DeepDiscriminativeEmbeddingsforDurationRobustSpeakerVeri%EF%AC%81cation20190821-446-67hczh.pdf)

- **Learning Discriminative Features for Speaker Identification and Verification**(2018), Sarthak Yadav et al. [[pdf]](https://pdfs.semanticscholar.org/ce8c/8e9fdbdd84adc096018bb0edb49b6913b946.pdf)

- **Large Margin Softmax Loss for Speaker Verification**(2019), Yi Liu et al. [[pdf]](https://arxiv.org/pdf/1904.03479)

- **Unsupervised feature enhancement for speaker verification**(2019), Phani Sankar Nidadavolu et al. [[pdf]](https://arxiv.org/pdf/1910.11915)

- **Feature enhancement with deep feature losses for speaker verification**(2019), Saurabh Kataria et al. [[pdf]](https://arxiv.org/pdf/1910.11905)

- **Generalized end2end loss for speaker verification**(2019), Li Wan et al. [[pdf]](https://arxiv.org/pdf/1710.10467.pdf)

- **Spatial Pyramid Encoding with Convex Length Normalization for Text-Independent Speaker Verification**(2019), Youngmoon Jung et al. [[pdf]](https://arxiv.org/pdf/1906.08333)

- **VoxSRC 2019: The first VoxCeleb Speaker Recognition Challenge**(2019), Son Chung et al. [[pdf]](https://arxiv.org/pdf/1912.02522)

- **BUT System Description to VoxCeleb Speaker Recognition Challenge 2019**(2019), Hossein Zeinali et al. [[pdf]](https://arxiv.org/pdf/1910.12592)


### Voice Conversion
- **Voice conversion using deep Bidirectional Long Short-Term Memory based Recurrent Neural Networks**(2015), Lifa Sun et al. [[pdf]](https://ieeexplore.ieee.org/document/7178896)

- **Phonetic posteriorgrams for many-to-one voice conversion without parallel data training**(2016), Lifa Sun et al. [[pdf]](https://ieeexplore.ieee.org/document/7552917)

- **StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks**(2018), Hirokazu Kameoka et al. [[pdf]](https://arxiv.org/pdf/1806.02169)

- **AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss**(2019), Kaizhi Qian et al. [[pdf]](http://proceedings.mlr.press/v97/qian19c.html)

- **StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion**(2019), Takuhiro Kaneko et al. [[pdf]](https://arxiv.org/pdf/1907.12279)

- **Unsupervised End-to-End Learning of Discrete Linguistic Units for Voice Conversion**(2019), Andy T. Liu et al. [[pdf]](https://arxiv.org/pdf/1905.11563)

- **Attention-Based Speaker Embeddings for One-Shot Voice Conversion**(2020), Tatsuma Ishihara et al. [[pdf]](http://www.interspeech2020.org/uploadfile/pdf/Mon-2-7-8.pdf)


- **F0-consistent many-to-many non-parallel voice conversion via conditional autoencoder**(2020), Kaizhi Qian et al. [[pdf]](https://arxiv.org/pdf/2004.07370)

- **Recognition-Synthesis Based Non-Parallel Voice Conversion with Adversarial Learning**(2020), Jing-Xuan Zhang et al. [[pdf]](http://www.interspeech2020.org/uploadfile/pdf/Mon-2-7-1.pdf)

- **An Improved StarGAN for Emotional Voice Conversion: Enhancing Voice Quality and Data Augmentation**(2021), Xiangheng He et al. [[pdf]](https://arxiv.org/pdf/2107.08361)

- **crank: An Open-Source Software for Nonparallel Voice Conversion Based on Vector-Quantized Variational Autoencoder**(2021), Kazuhiro Kobayashi et al. [[pdf]](https://arxiv.org/pdf/2103.02858)

- **CVC: Contrastive Learning for Non-parallel Voice Conversion**(2021), Tingle Li et al. [[pdf]](https://arxiv.org/pdf/2011.00782.pdf)

- **NoiseVC: Towards High Quality Zero-Shot Voice Conversion**(2021), Shijun Wang et al. [[pdf]](https://arxiv.org/pdf/2104.06074.pdf)

- **On Prosody Modeling for ASR+TTS based Voice Conversion**(2021), Wen-Chin Huang et al. [[pdf]](https://arxiv.org/pdf/2107.09477.pdf)

- **StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion**(2021), Yinghao Aaron Li et al. [[pdf]](https://arxiv.org/pdf/2107.10394.pdf)

- **Zero-shot Voice Conversion via Self-supervised Prosody Representation Learning**(2021), Shijun Wang et al. [[pdf]](https://arxiv.org/pdf/2110.14422)


### Speech Synthesis
- **Signal estimation from modified short-time Fourier transform**(1993), Daniel W. Griffin et al. [[pdf]](https://pdfs.semanticscholar.org/ade8/d1511a61d78948bb0d43e207593389935421.pdf?\_ga=2.229355228.1725061846.1494658711-1308334183.1494658711)

- **Text-to-speech synthesis**(2009), Paul Taylor et al. [[pdf]](https://books.google.com/books?hl=zh-CN&lr=&id=BFnkm-FpBAUC&oi=fnd&pg=PR9&dq=Text-to-Speech+Synthesis&ots=ucm6pVQ0bW&sig=1ZoIFILLQLbdHtJu0MlLHkmPnqE#v=onepage&q=Text-to-Speech%20Synthesis&f=false)

- **A fast Griffin-Lim algorithm**(2013), Nathanael Perraudin et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6701851)

- **TTS synthesis with bidirectional LSTM based recurrent neural networks**(2014), Yuchen Fan et al. [[pdf]](https://pdfs.semanticscholar.org/c217/905bc98f00af747e8e9d5f6b79fb89a90886.pdf)

- **First Step Towards End-to-End Parametric TTS Synthesis: Generating Spectral Parameters with Neural Attention**(2016), Wenfu Wang et al. [[pdf]](http://doi.org/10.21437/Interspeech.2016-134)

- **Recent Advances in Google Real-Time HMM-Driven Unit Selection Synthesizer**(2016), Xavi Gonzalvo et al. [[pdf]](http://doi.org/10.21437/Interspeech.2016-264)

- **SampleRNN: An Unconditional End-to-End Neural Audio Generation Model**(2016), Soroush Mehri et al. [[pdf]](https://arxiv.org/pdf/1612.07837.pdf)

- **WaveNet: A Generative Model for Raw Audio**(2016), Aäron van den Oord et al. [[pdf]](https://arxiv.org/pdf/1609.03499.pdf)

- **Char2Wav: End-to-end speech synthesis**(2017), J Sotelo et al. [[pdf]](https://openreview.net/forum?id=B1VWyySKx)

- **Deep Voice: Real-time Neural Text-to-Speech**(2017), Sercan O. Arik et al. [[pdf]](https://arxiv.org/pdf/1702.07825.pdf)

- **Deep Voice 2: Multi-Speaker Neural Text-to-Speech**(2017), Sercan Arik et al. [[pdf]](https://arxiv.org/pdf/1705.08947)

- **Deep Voice 3: 2000-Speaker Neural Text-to-speech**(2017), Wei Ping et al. [[pdf]](https://arxiv.org/pdf/1710.07654)

- **Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions**(2017), Jonathan Shen et al. [[pdf]](https://arxiv.org/pdf/1712.05884)

- **Parallel WaveNet: Fast High-Fidelity Speech Synthesis**(2017), Aaron van den Oord et al. [[pdf]](https://arxiv.org/pdf/1711.10433)

- **Statistical Parametric Speech Synthesis Using Generative Adversarial Networks Under A Multi-task Learning Framework**(2017), S Yang et al. [[pdf]](https://arxiv.org/pdf/1707.01670)

- **Tacotron: Towards End-to-End Speech Synthesis**(2017), Yuxuan Wang et al. [[pdf]](https://pdfs.semanticscholar.org/a072/c2a400f62f720b68dc54a662fb1ae115bf06.pdf?\_ga=2.133718478.1725061846.1494658711-1308334183.1494658711)

- **Uncovering Latent Style Factors for Expressive Speech Synthesis**(2017), Yuxuan Wang et al. [[pdf]](https://arxiv.org/pdf/1711.00520)

- **VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop**(2017), Yaniv Taigman et al. [[pdf]](https://arxiv.org/pdf/1707.06588)

- **ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech**(2018), Wei Ping et al. [[pdf]](https://arxiv.org/pdf/1807.07281.pdf)

- **Deep Feed-forward Sequential Memory Networks for Speech Synthesis**(2018), Mengxiao Bi et al. [[pdf]](https://arxiv.org/abs/1802.09194)

- **LPCNet: Improving Neural Speech Synthesis Through Linear Prediction**(2018), Jean-Marc Valin et al. [[pdf]](https://arxiv.org/pdf/1810.11846)

- **Learning latent representations for style control and transfer in end-to-end speech synthesis**(2018), Ya-Jie Zhang et al. [[pdf]](https://arxiv.org/pdf/1812.04342)

- **Neural Voice Cloning with a Few Samples**(2018), Sercan O. Arık et al. [[pdf]](https://arxiv.org/pdf/1802.06006.pdf)

- **Predicting Expressive Speaking Style From Text In End-To-End Speech Synthesis**(2018), Daisy Stanton et al. [[pdf]](https://arxiv.org/pdf/1808.01410)

- **Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis**(2018), Y Wang et al. [[pdf]](https://arxiv.org/pdf/1803.09017.pdf)

- **Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron**(2018), RJ Skerry-Ryan et al. [[pdf]](https://arxiv.org/pdf/1803.09047)

- **DurIAN: Duration Informed Attention Network For Multimodal Synthesis**(2019), Chengzhu Yu et al. [[pdf]](https://arxiv.org/pdf/1909.01700)

- **Fast spectrogram inversion using multi-head convolutional neural networks**(2019), SÖ Arık et al. [[pdf]](https://ieeexplore.ieee.org/abstract/document/8528831)

- **FastSpeech: Fast, Robust and Controllable Text to Speech**(2019), Yi Ren et al. [[pdf]](https://arxiv.org/pdf/1905.09263.pdf)

- **Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning**(2019), Yu Zhang et al. [[pdf]](https://arxiv.org/pdf/1907.04448)

- **MelNet: A Generative Model for Audio in the Frequency Domain**(2019), Sean Vasquez et al. [[pdf]](https://arxiv.org/pdf/1906.01083)

- **Multi-Speaker End-to-End Speech Synthesis**(2019), Jihyun Park et al. [[pdf]](https://arxiv.org/pdf/1907.04462)

- **MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis**(2019), Kundan Kumar et al. [[pdf]](https://arxiv.org/pdf/1910.06711.pdf)

- **Neural Speech Synthesis with Transformer Network**(2019), Naihan Li et al. [[pdf]](https://arxiv.org/pdf/1809.08895)

- **Parallel Neural Text-to-Speech**(2019), Kainan Peng et al. [[pdf]](https://arxiv.org/pdf/1905.08459.pdf)

- **Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram**(2019), Ryuichi Yamamoto et al. [[pdf]](https://arxiv.org/pdf/1910.11480)
<sub><sup>it comes out the same time as MelGAN, while no one refers to each other...Besides, I think the gaussian noise is unnecessary, since melspec has very strong information.</sup></sub>

- **Problem-Agnostic Speech Embeddings for Multi-Speaker Text-to-Speech with SampleRNN**(2019), David Alvarez et al. [[pdf]](https://arxiv.org/pdf/1906.00733.pdf)

- **Robust Sequence-to-Sequence Acoustic Modeling with Stepwise Monotonic Attention for Neural TTS**(2019), Mutian He et al. [[pdf]](https://arxiv.org/pdf/1906.00672.pdf)

- **Towards Transfer Learning for End-to-End Speech Synthesis from Deep Pre-Trained Language Models**(2019), Wei Fang et al. [[pdf]](https://arxiv.org/pdf/1906.07307.pdf)

- **Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis**(2019), Ye Jia et al. [[pdf]](https://arxiv.org/pdf/1806.04558.pdf)

- **WaveFlow: A Compact Flow-based Model for Raw Audio**(2019), Wei Ping et al. [[pdf]](https://arxiv.org/pdf/1912.01219)

- **Waveglow: A flow-based generative network for speech synthesis**(2019), R Prenger et al. [[pdf]](https://arxiv.org/pdf/1811.00002.pdf)

- **AlignTTS: Efficient Feed-Forward Text-to-Speech System without Explicit Alignmen**(2020), Zhen Zeng et al. [[pdf]](https://arxiv.org/pdf/2003.01950)

- **BOFFIN TTS: Few-Shot Speaker Adaptation by Bayesian Optimization**(2020), Henry B.Moss et al. [[pdf]](https://arxiv.org/pdf/2002.01953.pdf)

- **Bunched LPCNet : Vocoder for Low-cost Neural Text-To-Speech Systems**(2020), Ravichander Vipperla et al. [[pdf]](https://arxiv.org/pdf/2008.04574)

- **CopyCat: Many-to-Many Fine-Grained Prosody Transfer for Neural Text-to-Speech**(2020), Sri Karlapati et al. [[pdf]](https://arxiv.org/pdf/2004.14617)
    
- **EfficientTTS: An Efficient and High-Quality Text-to-Speech Architecture**(2020), Chenfeng Miao et al. [[pdf]](https://arxiv.org/pdf/2012.03500)

- **End-to-End Adversarial Text-to-Speech**(2020), Jeff Donahue et al. [[pdf]](https://arxiv.org/pdf/2006.03575)

- **FastSpeech 2: Fast and High-Quality End-to-End Text to Speech**(2020), Yi Ren et al. [[pdf]](https://arxiv.org/pdf/2006.04558)

- **Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis**(2020), Rafael Valle et al. [[pdf]](https://arxiv.org/pdf/2005.05957)

- **Flow-TTS: A Non-Autoregressive Network for Text to Speech Based on Flow**(2020), Chenfeng Miao et al. [[pdf]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9054484)

- **Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesis**(2020), Guangzhi Sun et al. [[pdf]](https://arxiv.org/pdf/2002.03785)

- **Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior**(2020), Guangzhi Sun et al. [[pdf]](https://arxiv.org/pdf/2002.03788)

- **Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search**(2020), Jaehyeon Kim et al. [[pdf]](https://arxiv.org/pdf/2005.11129)

- **HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis**(2020), Jungil Kong et al. [[pdf]](https://arxiv.org/pdf/2010.05646)

- **Location-Relative Attention Mechanisms For Robust Long-Form Speech Synthesi**(2020), Eric Battenberg et al. [[pdf]](https://arxiv.org/pdf/1910.10288)

- **MultiSpeech: Multi-Speaker Text to Speech with Transformer**(2020), Mingjian Chen et al. [[pdf]](https://arxiv.org/pdf/2006.04664.pdf)

- **Parallel Tacotron: Non-Autoregressive and Controllable TTS**(2020), Isaac Elias et al. [[pdf]](https://arxiv.org/pdf/2010.11439)

- **RobuTrans: A Robust Transformer-Based Text-to-Speech Model**(2020), Naihan Li et al. [[pdf]](https://aaai.org/ojs/index.php/AAAI/article/view/6337)

- **Text-Independent Speaker Verification with Dual Attention Network**(2020), Jingyu Li et al. [[pdf]](https://arxiv.org/pdf/2009.05485)

- **WaveGrad: Estimating Gradients for Waveform Generation**(2020), Nanxin Chen et al. [[pdf]](https://arxiv.org/pdf/2009.00713)

- **AdaSpeech: Adaptive Text to Speech for Custom Voice**(2021), Mingjian Chen et al. [[pdf]](https://arxiv.org/pdf/2103.00993)

- **A Survey on Neural Speech Synthesis**(2021), Xu Tan et al. [[pdf]](https://arxiv.org/pdf/2106.15561.pdf)

- **A Streamwise GAN Vocoder for Wideband Speech Coding at Very Low Bit Rate**(2021), Ahmed Mustafa et al. [[pdf]](https://arxiv.org/pdf/2108.04051.pdf)

- **Controllable cross-speaker emotion transfer for end-to-end speech synthesis**(2021), Tao Li et al. [[pdf]](https://arxiv.org/pdf/2109.06733.pdf)

- **Cloning one’s voice using very limited data in the wild**(2021), Dongyang Dai et al. [[pdf]](https://arxiv.org/pdf/2110.03347.pdf)

- **DiffWave: A Versatile Diffusion Model for Audio Synthesis**(2021), Zhifeng Kong et al. [[pdf]](https://arxiv.org/pdf/2009.09761)

- **Diff-TTS: A Denoising Diffusion Model for Text-to-Speech**(2021), Myeonghun Jeong et al. [[pdf]](https://arxiv.org/pdf/2104.01409.pdf)

- **Fre-GAN: Adversarial Frequency-consistent Audio Synthesis**(2021), Ji-Hoon Kim et al. [[pdf]](https://arxiv.org/pdf/2106.02297.pdf)

- **Full-band LPCNet: A real-time neural vocoder for 48 kHz audio with a CPU**(2021), Keisuke Matsubara et al. [[pdf]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9455356)

- **Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech**(2021), Vadim Popov et al. [[pdf]](https://arxiv.org/pdf/2105.06337.pdf)

- **High-fidelity and low-latency universal neural vocoder based on multiband WaveRNN with data-driven linear prediction for discrete waveform modeling**(2021), Patrick Lumban Tobing et al. [[pdf]](https://arxiv.org/pdf/2105.09856.pdf)

- **Hierarchical Prosody Modeling for Non-Autoregressive Speech Synthesis**(2021), Chung-Ming Chien et al. [[pdf]](https://arxiv.org/pdf/2011.06465.pdf)

- **ItoˆTTS and ItoˆWave: Linear Stochastic Differential Equation Is All You Need For Audio Generation**(2021), Shoule Wu et al. [[pdf]](https://arxiv.org/pdf/2105.07583.pdf)

- **Neural HMMs are all you need (for high-quality attention-free TTS)**(2021), Shivam Mehta et al. [[pdf]](https://arxiv.org/pdf/2108.13320)

- **Neural Pitch-Shifting and Time-Stretching with Controllable LPCNet**(2021), Max Morrison et al. [[pdf]](https://arxiv.org/pdf/2110.02360)

- **One TTS Alignment To Rule Them All**(2021), Rohan Badlani et al. [[pdf]](https://arxiv.org/pdf/2108.10447.pdf)

- **KaraTuner: Towards end to end natural pitch correction for singing voice in karaoke**(2021), Xiaobin Zhuang et al. [[pdf]](https://arxiv.org/pdf/2110.09121)

- **PnG BERT: Augmented BERT on Phonemes and Graphemes for Neural TTS**(2021), Ye Jia et al. [[pdf]](https://arxiv.org/pdf/2103.15060.pdf)

- **Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling**(2021), Isaac Elias et al. [[pdf]](https://arxiv.org/pdf/2103.14574)

- **Transformer-based Acoustic Modeling for Streaming Speech Synthesis**(2021), Chunyang Wu et al. [[pdf]](https://research.fb.com/wp-content/uploads/2021/06/Transformer-based-Acoustic-Modeling-for-Streaming-Speech-Synthesis.pdf)

- **Triple M: A Practical Neural Text-to-speech System With Multi-guidance Attention And Multi-band Multi-time Lpcnet**(2021), Shilun Lin et al. [[pdf]](https://arxiv.org/pdf/2102.00247)

- **TalkNet 2: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis with Explicit Pitch and Duration Prediction**(2021), Stanislav Beliaev et al. [[pdf]](https://arxiv.org/pdf/2104.08189)
<sub><sup>TalkNet2 has minor difference from TalkNet,so I don't include TalkNet here.</sup></sub>

- **Towards Multi-Scale Style Control for Expressive Speech Synthesis**(2021), Xiang Li et al. [[pdf]](https://arxiv.org/pdf/2104.03521)


### Language Modelling
- **Class-Based n-gram Models of Natural Language**(1992), Peter F. Brown et al. [[pdf]](https://pdfs.semanticscholar.org/ce84/cf6160ab221d5ee67afad046d2b89560749d.pdf?\_ga=2.197138663.999867306.1494660639-1308334183.1494658711)

- **An empirical study of smoothing techniques for language modeling**(1996), Stanley F. Chen et al. [[pdf]](https://dl.acm.org/ft\_gateway.cfm?id=981904&ftid=567802&dwn=1&CFID=843437542&CFTOKEN=31448020)

- **A Neural Probabilistic Language Model**(2000), Yoshua Bengio et al. [[pdf]](https://pdfs.semanticscholar.org/8d43/4a90b68fd0f2592d6fe7acf67d232123ad67.pdf?\_ga=2.262836293.895163446.1494660654-1308334183.1494658711)

- **A new statistical approach to Chinese Pinyin input**(2000), Zheng Chen et al. [[pdf]](https://dl.acm.org/ft\_gateway.cfm?id=1075249&ftid=261667&dwn=1&CFID=843437542&CFTOKEN=31448020)

- **Discriminative n-gram language modeling**(2007), Brian Roark et al. [[pdf]](https://pdfs.semanticscholar.org/b258/5b3cdcb81db887f756b8f90fd0e04f9ef952.pdf?\_ga=2.103398592.1137797782.1494660710-1308334183.1494658711)

- **Neural Network Language Model for Chinese Pinyin Input Method Engine**(2015), S Chen et al. [[pdf]](https://pdfs.semanticscholar.org/1294/f49cf1a7397f0423ec617a78c7995139bc5b.pdf)

- **Efficient Training and Evaluation of Recurrent Neural Network Language Models for Automatic Speech Recognition**(2016), Xie Chen et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7533441)

- **Exploring the limits of language modeling**(2016), R Jozefowicz et al. [[pdf]](https://arxiv.org/pdf/1602.02410)

- **On the State of the Art of Evaluation in Neural Language Models**(2016), G Melis et al. [[pdf]](https://arxiv.org/pdf/1707.05589)

- **Pay Less Attention with Lightweight and Dynamic Convolutions**(2019), Felix Wu et al.[[pdf]](https://arxiv.org/pdf/1901.10430)


### Confidence Estimates
- **Estimating Confidence using Word Lattices**(1997), T. Kemp et al. [[pdf]](http://www.cs.cmu.edu/~tschaaf/MyPublications/1997-eurospeech-kemp-schaaf.pdf)

- **Large vocabulary decoding and confidence estimation using word posterior probabilities**(2000), G. Evermann et al. [[pdf]](http://mi.eng.cam.ac.uk/reports/svr-ftp/evermann_icassp00.pdf)

- **Combining Information Sources for Confidence Estimation with CRF Models**(2011), M. S. Seigel et al. [[pdf]](https://pdfs.semanticscholar.org/d2e6/e51adb6f5d3f7a9265b2eb77b000358ebbab.pdf)

- **Speaker-Adapted Confidence Measures for ASR using Deep Bidirectional Recurrent Neural Networks**(2018), M. ́A. Del-Agua et al. [[pdf]](https://riunet.upv.es/bitstream/handle/10251/121369/double.pdf;jsessionid=F2A9322C712EA9DD1E29C56BD7E7CEA3?sequence=2)

- **Bi-Directional Lattice Recurrent Neural Networks for Confidence Estimation**(2018), Q. Li et al. [[pdf]](https://arxiv.org/pdf/1810.13024.pdf)

- **Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks**(2020), A. Kastanos et al. [[pdf]](https://arxiv.org/pdf/1910.11933.pdf)

- **CONFIDENCE ESTIMATION FOR ATTENTION-BASED SEQUENCE-TO-SEQUENCE MODELS FOR SPEECH RECOGNITION**(2020), Qiujia Li et al. [[pdf]](https://arxiv.org/pdf/2010.11428v2.pdf)

- **Residual Energy-Based Models for End-to-End Speech Recognition**(2021), Qiujia Li et al. [[pdf]](https://arxiv.org/pdf/2103.14152v1.pdf)

- **Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction**(2021), David Qiu et al. [[pdf]](https://arxiv.org/pdf/2104.12870.pdf)


### Music Modelling
- **Onsets and Frames: Dual-Objective Piano Transcription**(2017), Curtis Hawthorne et al. [[pdf]](https://arxiv.org/abs/1710.11153)

- **Unsupervised Singing Voice Conversion**(2019), Eliya Nachmani et al. [[pdf]](https://research.fb.com/wp-content/uploads/2019/09/Unsupervised-Singing-Voice-Conversion-v2.pdf)

- **ByteSing- A Chinese Singing Voice Synthesis System Using Duration Allocated Encoder-Decoder Acoustic Models and WaveRNN Vocoders**(2020), Yu Gu et al. [[pdf]](https://arxiv.org/pdf/2004.11012)

- **DurIAN-SC: Duration Informed Attention Network based Singing Voice Conversion System**(2020), Liqiang Zhang et al. [[pdf]](https://arxiv.org/pdf/2008.03009.pdf)

- **HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesis**(2020), Jiawei Chen et al. [[pdf]](https://arxiv.org/pdf/2009.01776)

- **Jukebox: A Generative Model for Music**(2020), Prafulla Dhariwal et al. [[pdf]](https://arxiv.org/pdf/2005.00341)

- **MLP Singer: Towards Rapid Parallel Korean Singing Voice Synthesis**(2021), Jaesung Tae et al. [[pdf]](https://arxiv.org/pdf/2106.07886.pdf)

- **MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training**(2021), Mingliang Zeng et al. [[pdf]](https://arxiv.org/pdf/2106.05630.pdf)

- **N-Singer: A Non-Autoregressive Korean Singing Voice Synthesis System for Pronunciation Enhancement**(2021), Gyeong-Hoon Lee et al. [[pdf]](https://arxiv.org/pdf/2106.15205.pdf)

- **PeriodNet: A non-autoregressive waveform generation model with a structure separating periodic and aperiodic components**(2021), Yukiya Hono et al. [[pdf]](https://arxiv.org/pdf/2102.07786)

- **Sequence-to-Sequence Piano Transcription with Transformers**(2021), Curtis Hawthorne et al. [[pdf]](https://arxiv.org/pdf/2107.09142.pdf)


### Interesting papers
- **The Reversible Residual Network: Backpropagation Without Storing Activations**(2017), Aidan N. Gomez et al. [[pdf]](https://arxiv.org/pdf/1707.04585)

- **Soft-DTW: a Differentiable Loss Function for Time-Series**(2018), Marco Cuturi et al. [[pdf]](https://arxiv.org/pdf/1703.01541.pdf)

- **FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow**(2019), Xuezhe Ma et al. [[pdf]](https://arxiv.org/pdf/1909.02480)

- **Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks**(2019), Santiago Pascual et al. [[pdf]](https://arxiv.org/pdf/1904.03416.pdf)

- **Self-supervised audio representation learning for mobile devices**(2019), Marco Tagliasacchi et al. [[pdf]](https://arxiv.org/pdf/1905.11796.pdf)

- **SinGAN: Learning a Generative Model from a Single Natural Image**(2019), Tamar Rott Shaham et al. [[pdf]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Shaham_SinGAN_Learning_a_Generative_Model_From_a_Single_Natural_Image_ICCV_2019_paper.pdf)

- **Audio2Face: Generating Speech/Face Animation from Single Audio with Attention-Based Bidirectional LSTM Networks**(2019), Guanzhong Tian et al. [[pdf]](https://arxiv.org/pdf/1905.11142)

- **Attention is Not Only a Weight: Analyzing Transformers with Vector Norms**(2020), Goro Kobayashi et al. [[pdf]](https://arxiv.org/pdf/2004.10102)